The course was delivered for the last time in September 2025 and has been formally replaced by the course "Advanced Neural Networks for Industrial Engineering", which offers a substantially revised syllabus and a different educational focus.

• Official site of the Master Degree in Industrial/Management Engineering

• Available Master's Theses

• Programme A.Y. 2025/2026.

  • • • Fundamentals of Machine Learning Methods:

Introduction to machine learning and data-driven modeling: data preparation; generalization; regularization and structural optimization. Overview of the main clustering and classification methods.

• • • • Neural networks and Deep Learning:

Overview of shallow feed-forward and recurrent neural networks. Introduction to Deep Learning, specific problems and solutions (double descent, vanishing/exploding gradient, barren plateau, dropout, ensembling, weight initialization). Deep feed-forward and recurrent neural networks. Generative and diffusive systems (GAN, VAE, etc.).

• • • • Introduction to Hyperdimensional Computing:

Fundamentals of hyperdimensional computing: data representation through high-dimensional vectors and their sparse distribution properties and robustness. Vector symbolic architectures and computational models for learning and memory. Binding and superposition methods for encoding complex information. Associative memory management and hybrid Quantum-HDC approaches. HDC-based learning strategies for classification, regression, pattern recognition and eXplainable AI problems. Practical applications of HDC in the domains of industrial and information engineering, time series analysis, anomaly detection, and resource-constrained embedded systems.

• • • • Introduction to Quantum Computing:

Introduction to quantum computing. Elementary unitary transformations, Quantum Gate Arrays. Main quantum optimization algorithms, adiabatic approaches, Quantum Approximate Optimization Algorithm (QAOA). Variational approaches, Quantum Machine Learning and Quantum Neural Networks. Theoretical and application comparisons with GPU Computing and parallel computing.

• • • • Hands-on practices using Python and Matlab:

        • • classification and clustering;

          • linear regression, overfitting and underfitting;

          • deep learning;

          • quantum programming and simulation;

          • quantum deep learning;

          • energy time series prediction; 

          • graph neural networks;

          • behavioral analysis.

      • Applications and case studies:

        • • prediction of renewable energy sources, intelligent energy systems, smart grids;

          • applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.);

          • behavioral analysis and biometrics;

          • analysis of materials and industrial processes;

          • machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks;

          • federated and distributed learning systems;

          • quantum neural networks, quantum optimization, and quantum generative models. 

  • Teaching Material:

    • • M. Schuld and F. Petruccione, Supervised Learning with Quantum Computers, Springer Nature, Switzerland, 2018

      • Notes, slides and handouts provided by the Teachers (see the program references):
              [01-Intro_ML]   [02-HandsOn_NN]   [03-Clustering]   [04-CNNs]   [05-DRNNs]
              [06-Prediction]   [07-HDC_VSA]   [08-GAI]   [09-Intro_QC]   [10-VQC]   [11-GPU]

      • Additional material on hands-on and case studies:
                [Matlab_NN]   [QAOA]   [Grover]   [Quantum_DL]     

      • Further reading (optional):

        • • C.C. Aggarwal,  Neural Networks and Deep Learning, Springer Cham, Switzerland, 2023

          • S. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson, NJ, USA, 2009

          • O. Simeone, An Introduction to Quantum Machine Learning for Engineers, arXiv preprint [2205.09510], 2022

NOTICE. For each type of communication or inquiries related to the course, students are kindly requested to send me an e-mail writing in the SUBJECT "Machine Learning IE" and in the text body the following data: name, surname and university ID number. I will try to answer as soon as possible.